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1. © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 826
Optimizing Blood Supply Chains: AI-Enabled Smart Blood Management
Solutions
Ambarish Shashank Kshirsagar1, Yash Mohan Phalle2
1Department of Computer Engineering
Vishwakarma Institute of Information Technology, Kondhwa, Pune, 411048, Maharashtra, India .
2Department of Computer Engineering
Vishwakarma Institute of Information Technology, Kondhwa, Pune, 411048, Maharashtra, India.
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Abstract -In the realm of modern healthcare, a robust
and timely supply of blood is indispensable, with one in
seven hospital admissions necessitating blood transfusions.
While blood banks play a pivotal role in this process,
emergencies often pose challenges in swiftly acquiring
specific blood types. To address this critical issue, our
project introduces an innovative Blood Management
Application. This application integrates cutting-edge AI
technology to predict future blood demand based on
extensive historical data. By employing machine learning
models, our system alerts blood banks when the forecasted
demand is likely to surpass the available supply, enabling
proactive measures to be taken. Furthermore, our
application features a targeted blood request system, driven
by AI algorithms trained on donor data. This intelligent
feature prioritizes blood requests based on a donor's past
history, ensuring that urgent requirements are met swiftly
and efficiently. Through real-time alerts hospitals can
promptly mobilize donors, saving crucial time and enabling
timely, life-saving treatments.
In our platform donors can pledge their commitment to
donating blood, fostering a supportive community network.
By harnessing the potential of AI-driven predictions and
targeted requests, our Blood Management Application not
only revolutionizes blood supply chain management but also
ensures a rapid, reliable, and targeted preventive response.
Key Words:
Blood supply chain management, Demand supply
prediction models, Predictive Analytics, Healthcare
Technology, AI in Healthcare, Blood Bank Inventory
Management
1.INTRODUCTION
In the ever-evolving landscape of modern healthcare, the
importance of a robust and timely blood supply cannot be
overstated. With one in seven hospital admissions
necessitating blood transfusions, the need for a
streamlined and responsive blood management system is
paramount. Blood banks, the backbone of this critical
process, often face challenges, and are regularly under
supplied in times of need or overstacked which leads to
wastage of blood.
To address this pressing issue, our idea introduces the
Smart Blood Management System, a solution designed to
transform the way blood supply chain management
operates. At its core, our system integrates AI technology,
leveraging the power of predictive analytics based on
extensive historical data. By employing sophisticated
machine learning models, our system tries to accurately
forecast future blood demand, enabling proactive
measures to be taken when the anticipated demand is
likely to surpass the available supply. This foresighted
approach ensures that hospitals and blood banks are well-
prepared to meet the imminent needs of patients, saving
invaluable lives in the process.
We have recognized the critical role of blood donors in this
equation as well, our application features an intelligent
and targeted blood request system. Driven by AI
algorithms trained on available donor data, this feature
analyses donors' past history to prioritise blood requests.
By identifying the most suitable donors swiftly and
efficiently, urgent requirements are met with speed,
enabling timely, life-saving treatments and significantly
improving patient outcomes.
In our application, we have implemented a feature that
allows users to request blood from donors during
emergencies. This functionality proves invaluable in times
of urgent need, enabling users to initiate smart and
targeted requests to potential donors identified through
our model.
In essence, our Blood Management Application represents
more than just a technological advancement; it is a
transformative approach to healthcare. By integrating AI-
driven predictions, targeted donor requests, and
community engagement, our application redefines the
blood supply chain management paradigm. It ensures a
rapid, reliable, and targeted response to blood
requirements, safeguarding lives and providing hope in
times of medical crises.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072

2. © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 827
2. LITERATURE REVIEW:
The existing literature on emergency blood supply
systems reveals several deficiencies in the conventional
methods employed by hospitals and blood banks. In times
of crisis, the current approach involves a step-by-step
process: hospitals check their inventory first, and if the
required blood isn't available, they contact nearby blood
banks. If unsuccessful, the search expands to distant
facilities, patient relatives, or regular donors. This method,
while established, is undeniably time-consuming and
inefficient, often leading to delays in critical medical
treatments.
In analysing the existing research landscape, it becomes
evident that the area of blood supply chain management
requires significant attention, as highlighted in [3]. This
study indicates the need for extensive work in the supply
management domain, emphasising the gaps in the existing
methodologies and the urgency for innovation.
[1][5][7] have collectively made strides in centralising
databases, encompassing comprehensive information
about blood banks, donors, and blood donation camps.
This digital transformation represents a noteworthy
departure from the traditional practice of maintaining
manual log journals. Building on this progress, [4] and [6]
have introduced a Geographic Information System (GIS)
approach. This geospatial technology enhances the
effectiveness of retrieving information from blood bank
databases, enabling more precise and rapid location-based
searches for blood donors and supplies.
Furthermore, [2][4] have developed platforms enabling
users to search for blood in nearby blood banks based on
specific requirements, marking a significant advancement
from the conventional methods. Taking a step further, [6]
introduced a feature that allows users to request blood
even when it is unavailable in the immediate vicinity,
showcasing the evolving nature of technology in
addressing emergent needs.
However, despite these advancements, there remains a
pressing need for extensive research and development in
the following critical areas:
1. Establishment of a Centralised and AI-Enabled
System for Automated Blood Inventory
Management: Creating an intelligent, centralised
system that utilises AI algorithms to predict, monitor,
and manage blood inventory in real time is
paramount. This system should factor in various
variables such as blood type, demand patterns, expiry
dates, and geographic distribution to ensure optimal
blood supply at all times.
2. Development of Methods to Broaden the Scope of
Blood Search Efforts: Innovative approaches are
required to expand the reach of blood search efforts.
This could involve harnessing social media platforms,
community outreach programs, and incentives to
encourage regular blood donations.
In summary, while progress has been made, the
complexities of emergency blood supply demand
continuous exploration and innovation. By focusing on the
areas outlined above, researchers and practitioners can
collectively contribute to the development of more
efficient, responsive, and inclusive emergency blood
supply systems, ultimately saving lives and improving
healthcare outcomes.
3.PROPOSED SYSTEM:
As we have discovered main problems are centred around:
1. Establishment of a centralised and AI-enabled system
for automated blood inventory management.
2. Development of methods to broaden the scope of
blood search efforts
To address these problems we are proposing our system:
1. An Automated AI Enabled system for managing Blood
inventory
2. Smart Donor Identification and targeted efforts
3. Platform Features
a. Display Real-time data of available bloodstock
b. Request Blood
c. Organizing a blood donation camp
d. Blood bank directory
e. Dashboard for blood banks to track the
predicted demand and predicted expected
supply.
f. Alert system when predicted demand will be
more than predicted supply.
1)Automated AI-Enabled Blood Inventory
Management:
For Inventory management, It is crucial to account for the
expected demand and expected supply of blood taking into
account several factors like geolocation, blood group/type,
timestamp, unit requirement, and expiry information for
different blood components (plasma, RBC, WBC, platelets).
The Demand Model predicts blood demand for the
upcoming predefined interval, taking into account various
factors. On the other hand, the Supply Model forecasts the
expected blood donations for the same period.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072

3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 828
Demand Prediction Model: Utilizing historical data, our
AI system forecasts blood demand for upcoming intervals.
Factors considered include cyclic patterns in blood
donation, seasonal trends, and area-specific demand
variations.
Supply Prediction Model: Predicts expected blood
donations for predefined intervals. By comparing demand
and supply forecasts, the system identifies potential
shortages and surpluses.
If the demand exceeds the supply, the system will pinpoint
specific geographical locations where certain blood groups
are anticipated to be in short supply in the upcoming days,
along with the extent of scarcity measured in quantities.
2) Smart Donor Identification and Targeted Efforts:
To address immediate blood supply needs, it is highly
effective to promptly identify the most compatible donors
and prioritize them for expedited blood donation. Our AI-
powered donor identification system ensures efficient and
timely outreach to potential donors.
Donor Identification Model: Utilizes a dataset comprising
donor information such as age, blood type, donation
history, area, and health status. AI algorithms identify the
most suitable donors based on their history and
compatibility.
Targeted Blood Requests: Prioritises potential donors for
targeted blood requests. Blood banks and individual users
can utilise this feature to send requests to the most likely
donors.
3) Platform Features:
Fig. 1- Platform Architecture
1. Real-time Blood Stock Display: Displays live data of
available blood stock, enabling real-time decision-
making.
2. Blood Request Functionality: Allows users to
request blood based on their requirements,
connecting them with potential donors.
3. Organizing Blood Donation Camps: Enables Blood
banks to facilitate the organization of blood donation
camps, optimizing donor participation.
4. Blood Bank Directory: Provides a comprehensive
directory of blood banks, enhancing accessibility and
coordination.
5. Dashboard for blood banks:track the predicted
demand and predicted expected supply.
6. Alert system: Alter system in place to notify the blood
bank when predicted demand will be more than
predicted supply.
In summary, our proposed system combines cutting-edge
AI technology with a user-friendly interface, addressing
the challenges of blood inventory management and donor
engagement. By integrating predictive analytics, targeted
donor requests, and real-time alerts, our solution ensures
a proactive, efficient, and reliable blood supply chain
management process.
4. Results and Discussion
The prototype was developed by leveraging advanced AI
algorithms and predictive analytics to enhance and
improve blood supply chain management. The prototype
of AI-Enabled Smart Blood Management Systems was
implemented and tested on a smaller scale. Harnessing
extensive historical data, the system adeptly predicted
future blood demand and efficiently managed blood
inventory. The Demand Prediction Model, incorporating
cyclic patterns, seasonal trends, and area-specific
variations, alongside the Supply Prediction Model,
accurately forecasted expected blood donations for
predefined intervals. The AI-driven Donor Identification
Model prioritized compatible donors based on
comprehensive historical data, ensuring precise and
timely blood requests.
The prototype's performance, validated with real-time
data, has showcased significant accuracy in predicting the
dynamic patterns of blood demand and supply.
Furthermore, the prototype has demonstrated a decent
accuracy in identifying the most suitable blood donors as
well, ensuring efficient matching between donors and
recipients. Together, these results signify the prototype's
potential to significantly enhance the efficiency and
effectiveness of blood management within healthcare
organizations.
5. CONCLUSIONS
In summary, our research emphasizes the crucial role of
an efficient blood management system in modern
healthcare. We introduced the Smart Blood Management
System, integrating AI and predictive analytics to address
existing challenges faced by traditional blood banks.

4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 829
Through real-time stock monitoring, targeted donor
requests, and predictive analysis, our system ensures swift
responses to urgent blood needs. By fostering community
engagement and shared responsibility among donors, our
solution bridges gaps in the conventional methods,
safeguarding lives.
While our system provides a robust foundation,
continuous adaptation to evolving healthcare technologies
is essential. Ongoing efforts and collaborations can further
enhance our system, making substantial contributions to
advancing healthcare practices and ensuring timely,
adequate support for patients in need.
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Accessed 21 Oct. 2023.
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